کاربرد الگوریتم K-Star در پیش‌بینی تراز آب زیرزمینی (مطالعه موردی: دشت آسپاس)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه شهید چمران اهواز

2 گروه هیدرلوژی و منابع آب، دانشکده مهندسی علوم آب، دانشگاه شهید چمران اهواز، ایران

3 استادیار دانشگاه شهید چمران اهواز

چکیده

در این مطالعه تراز سطح آب زیرزمینی دشت آسپاس واقع در شمال غربی حوضه آبریز دریاچه‌‌های طشک-بختگان مهارلو در استان فارس با استفاده از داده‌‌های 39 پیزومتر در محدوده آبخوان در دوره آماری 96-1380 شبیه‌‌سازی شد. در این خصوص از دو مدل K-Star و ANN با تأخیرهای 1، 2 و 3 در دو فاز آموزش و آزمایش استفاده شد. مدل ANN در این مطالعه علاوه بر تأخیرهای یاد شده با تعداد لایه پنهان و نورون متفاوت نیز مورد بررسی قرار گرفت. با توجه به افت و نوسانات سطح آب زیرزمینی در دوره آماری یاد شده، آبخوان مورد مطالعه به 4 منطقه a، b، c و d تقسیم شد. نتایج شبیه‌‌سازی تراز سطح آب زیرزمینی در منطقه a نشان داد که مدل K-Star با 2 تأخیر در این منطقه عملکرد بهتری نسبت به مدل ANN در تأخیرهای مختلف داشته است. اما در سه منطقه دیگر مدل ANN با تأخیر 1 بهترین عملکرد را ارائه کرده است. مدل ANN با 2 لایه پنهان و 4 نورون در منطقه b، با 2 لایه پنهان و 6 نورون در منطقه c و با 4 لایه پنهان و 5 نورون در منطقه d بهترین الگو برای شبیه‌‌سازی مقادیر تراز سطح آب زیرزمینی معرفی شدند. به‌طور متوسط الگوهای برتر معرفی شده در مناطق b، c و d توانستند میزان خطا را نسبت به مدل K-Star به ترتیب حدود 57، 42 و 81 درصد بهبود بخشند. عملکرد ضعیف مدل K-Star در دو منطقه b و d نیز به‌وضوح قابل مشاهده می‌‌باشد. به‌‌طور کلی نتایج نشان داد که تأخیر 1 مدل ANN در مناطق سه‌گانه b، c و d با تعداد لایه پنهان و نرون‌‌های مختلف بهترین عملکرد را در شبیه‌‌سازی مقادیر تراز سطح ایستابی داشته است.

کلیدواژه‌ها

عنوان مقاله [English]

Application of K-star Algorithm for Groundwater Level Forecasting (Case study: Aspas plain)

نویسندگان [English]

  • Navid Rahnama 1
  • Heidar Zarei 2
  • Farshad Ahmadi 3
1 Shahid Chamran University of Ahvaz
2 Department of Hydrology and Water Resources, Faculty of Water Sciences, Shahid Chamran University of Ahvaz, Iran
3 Shahid Chamran university of Ahvaz
چکیده [English]

In this study, the groundwater level of Aspas Plain, located in the northwest of the catchment area of Tashk and Bakhtegan Lakes in Fars province, was simulated using the data of 39 piezometers in the aquifer plain in the period of 2001-2017. In this regard, K-Star and ANN models with 1, 2 and 3 lags were used in two phases of training and testing. ANN model in this study was also investigated with the number of hidden layers and different neurons in addition to the mentioned lags. According to the drop and fluctuations of the groundwater level in the mentioned statistical period, the studied aquifer was divided into 4 regions a, b, c and d. The results of the simulation of the groundwater level in area “a” showed that the K-Star model with 2 lags performed better than the ANN model in different lags. But in the other three regions, the ANN model with a lag-1 has provided the best performance. ANN model with 2 hidden layers and 4 neurons in region b, with 2 hidden layers and 6 neurons in region c, and with 4 hidden layers and 5 neurons in region d were introduced as the best model for simulating groundwater level values. On average, the best models introduced in regions b, c and d were able to improve the error rate by 57, 42 and 81%, respectively, compared to the K-Star model. The poor performance of the K-Star model in two regions b and d is also clearly visible. In general, the results showed that lag-1 of the ANN model had the best performance in simulating the values of the groundwater level in the three regions b, c and d with the number of hidden layers and different neurons.

کلیدواژه‌ها [English]

  • Annual Decline
  • Artificial Neural Network
  • Fluctuation of Groundwater
  • K-Star Model
  • Piezometer

مراجع

Afkhamifar, S. and Sarraf, A. 2020. Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization. Watershed Engineering and Management. 12(2): 351-364. doi: 10.22092/ijwmse.2019.126515.1669
Banadkooki, F. B., Ehteram, M., Ahmed, A. N., Teo, F. Y., Fai, C. M., Afan, H. A. and El-Shafie, A. 2020. Enhancement of groundwater-level prediction using an integrated machine learning model optimized by whale algorithm. Natural Resources Research. 29(5): 3233-3252.
Bustami, R., Bessaih, N., Bong, C. and Suhaili, S. 2007. Artificial Neural Network for Precipitation and Water Level Predictions of Bedup River. IAENG International Journal of Computer Science. 34(2).
Charles, J., Vinodhini, G. and Nagarajan, R. 2021. A Machine Learning based Variational Autoencoder Model for Water Quality Prediction. Journal of Green Engineering. 1: 2029-2040.
Cleary, J. G. and Trigg, L. E. 1995. K*: An instance-based learner using an entropic distance measure. In Machine Learning Proceedings. 108-114. Morgan Kaufmann.
Dawson, C. W. and Wilby, R. L. 2001. Hydrological modelling using artificial neural networks. Progress in Physical Geography. 25(1): 80-108.
Eslami, P., Nasirian, A., Akbarpour, A. and Nazeri Tahroudi, M. 2022. Groundwater estimation of Ghayen plain with regression-based and hybrid time series models. Paddy and Water Environment. 1-12.
Garcia-Bartual, R. 2002. Short term river flood forecasting with neural networks. 1st international congress on environmental modelling and software - lugano, Switzerland - June 2002
Ghourdoyee Milan, S., Aryaazar, N., Javadi, S., Razdar, B. 2020. Simulation of groundwater head using LS-SVM and comparison with ANN & MLR. Hydrogeology. 5(1): 118-133. Doi: 10.22034/hydro.2020.10455
Granata, F., Di Nunno, F., Gargano, R. and de Marinis, G. 2019. Equivalent discharge coefficient of side weirs in circular channel—a lazy machine learning approach. Water. 11(11): 2406.
Haykin, S. 1999. Neural Networks, a comprehensive foundation, Prentice-Hall Inc. Upper Saddle River, New Jersey. 7458, 161-175.
Huang, W., Murray, C., Kraus, N. and Rosati, J. 2003. Development of a regional neural network for coastal water level predictions. Ocean Engineering. 30(17): 2275-2295.
Iqbal, M., Naeem, U. A., Ahmad, A., Ghani, U. and Farid, T. 2020. Relating groundwater levels with meteorological parameters using ANN technique. Measurement. 166: 108163.
Jaafari, A., Panahi, M., Pham, B. T., Shahabi, H., Bui, D. T., Rezaie, F. and Lee, S. 2019. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. Catena. 175: 430-445.
Khedri, A., Kalantari, N. and Vadiati, M. 2020. Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer. Water Supply. 20(3): 909-921.
Khosravi, K., Daggupati, P., Alami, M. T., Awadh, S. M., Ghareb, M. I., Panahi, M. and Yaseen, Z. M. 2019. Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq. Computers and Electronics in Agriculture. 167: 105041.
Kombo, O. H., Kumaran, S., Sheikh, Y. H., Bovim, A. and Jayavel, K. 2020. Long-term groundwater level prediction model based on hybrid KNN-RF technique. Hydrology. 7(3): 59.
Krause, P., Boyle, D. P. and Bäse, F. 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences. 5: 89-97.
Lallahem, S., Mania, J., Hani, A. and Najjar, Y. 2005. On the use of neural networks to evaluate groundwater levels in fractured media. Journal of Hydrology. 307(1-4): 92-111.
Maroufpoor, S., Bozorg-Haddad, O. and Maroufpoor, E. 2020. Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: Hybridization of artificial neural network with grey wolf optimizer algorithm. Journal of Hydrology. 588: 125060.
McCulloch, W. S. and Pitts, W. 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of Mathematical Biophysics. 5(4): 115-133.
Mirarabi, A., Nassery, H. R., Nakhaei, M., Adamowski, J., Akbarzadeh, A. H. and Alijani, F. 2019. Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems. Environmental Earth Sciences. 78(15): 1-15.
Nash, J. E. and Sutcliffe, J. V. 1970. River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology. 10(3): 282-290.
Nazeri-Tahroudi, M. and Ramezani, Y. 2020. Estimation of dew point temperature in different climates of Iran using support vector regression.  Quarterly Journal of the Hungarian Meteorological Service. 124(4): 521-539.
Nguyen, P. T., Ha, D. H., Avand, M., Jaafari, A., Nguyen, H. D., Al-Ansari, N. and Pham, B. T. 2020. Soft computing ensemble models based on logistic regression for groundwater potential mapping. Applied Sciences. 10(7): 2469.
Nhu, V. H., Mohammadi, A., Shahabi, H., Shirzadi, A., Al-Ansari, N., Ahmad, B. B. and Nguyen, H. 2020. Monitoring and assessment of water level fluctuations of the Lake Urmia and its environmental consequences using multitemporal Landsat 7 ETM+ images. International Journal of Environmental Research and Public Health. 17(12): 4210.
Pham, B. T., Jaafari, A., Prakash, I., Singh, S. K., Quoc, N. K. and Bui, D. T. 2019. Hybrid computational intelligence models for groundwater potential mapping. Catena. 182: 104101.
Raji, M., Tahroudi, M. N., Ye, F. and Dutta, J. 2022. Prediction of heterogeneous Fenton process in treatment of melanoidin-containing wastewater using data-based models. Journal of Environmental Management. 307: 114518.
Salari, S., Moghaddasi, M., Mohammadi Ghaleni, M. and Akbari, M. 2021. Groundwater Level Prediction in Golpayegan Aquifer Using ANFIS and PSO Combination. Iranian Journal of Soil and Water Research. 52(3): 721-732. Doi: 10.22059/ijswr.2021.314323.668814.
Sharafati, A., Asadollah, S. and Neshat, A. 2020. A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. Journal of Hydrology. 591: 125468.
Tabatabaei, S. M., Nazeri Tahroudi, M. and Hamraz, B. S. 2021. Comparison of the performances of GEP, ANFIS, and SVM artifical intelligence models in rainfall simulaton. Quarterly Journal of the Hungarian Meteorological Service. 125(2): 195-209.
Taormina, R., Chau, K. W. and Sethi, R. 2012. Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Engineering Applications of Artificial Intelligence. 25(8): 1670-1676.
Thirumalaiah, K. and Deo, M. C. 2000. Hydrological forecasting using neural networks. Journal of Hydrologic Engineering. 5(2): 180-189.
Tokar, A. S. and Johnson, P. A. 1999. Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering. 4(3): 232-239.
Wright, N. G. and Dastorani, M. T. 2001. Effects of river basin classification on artificial neural networks based ungauged catchment flood prediction. 3rd International Symposium on Environmental HydraulicsAt: Phoenix, Arizona.
Wright, N. G., Dastorani, M. T., Goodwin, P. and Slaughter, C. W. 2002. A combination of neural networks and hydrodynamic models for river flow prediction. In Fifth International Conference on Hydroinformatics, Cardiff, UK
Xu, Z., Huang, X., Lin, L., Wang, Q., Liu, J., Yu, K. and Chen, C. 2020. BP neural networks and random forest models to detect damage by Dendrolimus punctatus Walker. Journal of Forestry Research. 31(1): 107-121.
Yadav, B., Gupta, P. K., Patidar, N. and Himanshu, S. K. 2020. Ensemble modelling framework for groundwater level prediction in urban areas of India. Science of the Total Environment. 712: 135539.
Zhou, Y. 2009. A critical review of groundwater budget myth, safe yield and sustainability. Journal of Hydrology. 370(1-4): 207-213
نشریه آبیاری و زهکشی ایران
دوره 17، شماره 2 - شماره پیاپی 98
خرداد و تیر 1402
صفحه 305-320

فایل ها

هم رسانی

ارجاع به این مقاله

آمار